Chapter 5. Conclusions and Future Scope

Transcription

1 Chapter 5 Conclusions and Future Scope

2 CONCLUSIONS AND FUTURE SCOPE 5 The proposed method is evaluated on the FACE94 and other databases. It is shown that the proposed method outperforms all the compared state-of-the-art and baseline algorithms, which illustrates the robustness of the proposed method against the appearance variations of expression, lighting etc. The proposed method hopefully can inspire a new thinking and new way to tackle the face recognition problem. Performance of the proposed face recognition scheme has been tested upon three standard face databases, namely, the FACE94, ORL database and the YALE database. Extensive experiments are carried out in order to demonstrate the effectiveness of the proposed method for face recognition using proposed feature vectors. The recognition performance is investigated for different standard face databases consisting a range of different face images varying in facial expressions, lighting effects, and presence/absence of accessories. The performance of the proposed method in terms of recognition accuracy is obtained and compared with that of some recent methods. With the progress of time-frequency localization techniques, the robustness in face images based on signal processing techniques became comparable with statistical techniques. Spatial/frequency methods are suitable as there is variation in size, orientation and frequency of natural textures, Spatial/Spatial frequency methods are based on image representations that indicate frequency content in localized regions in the spatial domain. These methods overcome the shortcomings of the traditional Fourier based techniques. Such methods are able to achieve good localization in both the domains. They are consistent to human visual system theory. In this work, Wavelet based methods have been investigated. Using Daubechies wavelets, five level pyramidal decomposition has been implemented. Performance of all the methods presented in the thesis has been carried out. The method has been tested using three different classification schemes, and it has

3 Development of Feature Extraction Techniques for Face Recognition been proven to perform satisfactorily. FRR, FAR, Percentage Recognition has been considered for comparison. The recognition using DWT is 94%, by Entropy+Chi Square test it is 98%, by Entropy, Mutual information the result is 100%. This result is obtained with FFNN classifier. The result of Entropy, Mutual information, Chi- Square test, Entropy+Chi Square test is 98%, 92%, 80% and 94% respectively. This percentage of recognition is obtained with SOM classifier. Statistical methods such as Chi Square test have been studied. For face recognition, a new technique been developed. Feature performance has been tested by classifying the image dataset. Supervised and unsupervised classifier has been used to test the performance of all the features extracted in different methods. Entropy based features give comparable results with reduced computational burden. Due to its excellent performance, we expect that the proposed Entropy, Mutual information and Chi-Square test is applicable to other object recognition tasks as well. There are several directions where this work can be extended. Concept of Soft Computing can be used for automatic face recognition system. Using Soft Computing, neural network can be combined with fuzzy logic to enhance the performance of face recognition. Another avenue for research would be to implement other feature extraction technique on the same data set. In future, two or more classifiers can be combined to achieve better results. 125 Faculty of Computer Science and Engineering

4 LIST OF PUBLICATIONS 1. S.N. Kakarwal, Dr. R.R. Deshmukh, Performance Analysis of Face Recognition by Principal Component Analysis and Feed Forward Neural Network, International Journal of Engineering Innovations and Research, Vol.1 Issue 1, ISSN (online), pp , S.N. Kakarwal, Dr. R.R. Deshmukh, Hybrid Feature Extraction Technique for Face Recognition, International Journal of Advance Computer Science and Applications, Vol.3, No.2, ISSN (Online), 2012, pp S.N. Kakarwal, Dr. R.R. Deshmukh, Wavelet Transform based Feature Extraction for Face Recognition, International Journal of Computer Science and Applications, Issue-I, ISSN , pp , Jun S.N. Kakarwal, Dr. R.R. Deshmukh, Information Theory and Neural Network in based Approach for Face Recognition: A Review, International Journal of Recent Trends in Engineering, Vol. 2, No. 4, pp , Nov S.N. Kakarwal, Dr. R.R. Deshmukh, Face Recognition using Supervised Learning, WorldComp 2012, Las Vegas,USA, Jul S.N. Kakarwal, Dr. R.R. Deshmukh, Diverse Classifier for Face Recognition, International Conference ICKE 2011, ISBN , pp S.N. Kakarwal, S.D. Sapkal, Dr. R.R. Deshmukh, Analysis of Retina Recognition by Correlation and SVD, 2 nd International Conference on Advances in Computer Vision and Information Technology, pp , Dec S.N. Kakarwal, M.D. Malkauthekar, S.D. Sapkal, Dr. R.R. Deshmukh, Face Recognition Using FD and FFNN, IEEE International Advance Computing Conference at Patiala, pp , Mar

5 Development of Feature Extraction Techniques for Face Recognition 9. S.N. Kakarwal, Dr. V.R. Ratnaparkhe, Dr. R.R. Deshmukh, Multimodal Biometric Recognition based on Decision Level using Wavelet Transform, J.T. Mahajan College of Engg., Jalgaon, pp. 23, S.N. Kakarwal, S.D. Sapkal, Dr. R.R. Deshmukh, Face Recognition using Probabilistic Neural Network, National Level Conference on Recent Trends in Computers and Communications at Vidyalankar Institute of Technology, Mumbai, pp , Faculty of Computer Science and Engineering

6 International Journal of Engineering Innovation & Research Volume 1, Issue 1, ISSN : Performance Analysis of Face Recognition by Principal Component Analysis and Feed Forward Neural Network S.N. Kakarwal, R.R. Deshmukh Abstract In face recognition, it is important to select the invariant facial features especially faces with various poses and expression change. This paper presents novel technique for recognizing faces viz. PCA + FFNN. The experiment is performed over FERET faces. This technique gives results with considerable accuracy. Key Words Biometric, PCA and FFNN, Pattern Matching, FERET. I. INTRODUCTION Face recognition from still images and video sequence has been an active research area due to both its scientific challenges and wide range of potential applications such as biometric identity authentication, human-computer interaction, and video surveillance. Within the past two decades, numerous face recognition algorithms have been proposed as reviewed in the literature survey. Even though we human beings can detect and identify faces in a cluttered scene with little effort, building an automated system that accomplishes such objective is very challenging. The challenges mainly come from the large variations in the visual stimulus due to illumination conditions, viewing directions, facial expressions, aging, and disguises such as facial hair, glasses, or cosmetics [1]. Face Recognition focuses on recognizing the identity of a person from a database of known individuals. Face Recognition will find countless unobtrusive applications such as airport security and access control, building surveillance and monitoring Human-Computer Intelligent interaction and perceptual interfaces and Smart Environments at home, office and cars [2]. Within the last decade, face recognition (FR) has found a wide range of applications, from identity authentication, access control, and face-based video indexing/browsing, to human-computer interaction. Two issues are central to all these algorithms: 1) feature selection for face representation and 2) classification of a new face image based on the chosen feature representation. This work focuses on the issue of feature selection. Among various solutions to the problem, the most successful are those appearance-based approaches, which generally operate directly on images or appearances of face objects and process the images as two-dimensional (2-D) holistic patterns, to avoid difficulties associated with threedimensional (3-D) modeling, and shape or landmark detection [3]. The initial idea and early work of this research have been published in part as conference papers in [4], [5] and [6]. A recognition process involves a suitable representation, which should make the subsequent processing not only computationally feasible but also robust to certain variations in images. One method of face representation attempts to capture and define the face as a whole and exploit the statistical regularities of pixel intensity variations [7]. The remaining part of this paper is organized as follows. Section II extends to the pattern matching which also introduces and discusses the Principal Component Analysis and FFNN in detail. In Section III, extensive experiments on FERET are conducted to evaluate the performance of the proposed method on face recognition. Finally, conclusions are drawn in Section IV with some discussions. II. PATTERN MATCHING A. Pattern Recognition Methods During the past 30 years, pattern recognition has had a considerable growth. Applications of pattern recognition now include: character recognition; target detection; medical diagnosis; biomedical signal and image analysis; remote sensing; identification of human faces and of fingerprints; machine part recognition; automatic inspection; and many others. Traditionally, Pattern recognition methods are grouped into two categories: structural methods and feature space methods. Structural methods are useful in situation where the different classes of entity can be distinguished from each other by structural information, e.g. in character recognition different letters of the alphabet are structurally different from each other. The earliest-developed structural methods were the syntactic methods, based on using formal grammars to describe the structure of an entity [8]. The traditional approach to feature-space pattern recognition is the statistical approach, where the boundaries between the regions representing pattern classes in feature space are found by statistical inference based on a design set of sample patterns of known class membership [8]. Feature-space methods are useful in situations where the distinction between different pattern classes is readily expressible in terms of numerical measurements of this kind. The traditional goal of feature extraction is to characterize the object to be recognized by measurements whose values are very similar for objects in the same category, and very different for objects in different categories. This leads to the idea of seeking distinguishing features that are invariant to irrelevant transformations of the input. The task of the classifier Copyright 2012 IJEIR, All right reserved 40

7 (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. XXX, No. XXX, 2011 Hybrid Feature Extraction Technique for Face Recognition Sangeeta N. Kakarwal Department of Computer Science and Engineering P.E.S. College of Engineering Aurangabad, India Ratnadeep R. Deshmukh Department of Computer Science and IT Dr. Babasaheb Ambedkar Marathwada University Aurangabad, India Abstract This paper presents novel technique for recognizing faces. The proposed method uses hybrid feature extraction techniques such as Chi square and entropy are combined together. Feed forward and Self organizing neural network are used for classification. We evaluate proposed method using FACE94 and ORL database and achieved better performance. Keywords-Biometric, Chi square test, Entropy, FFNN and SOM I. INTRODUCTION Face recognition from still images and video sequence has been an active research area due to both its scientific challenges and wide range of potential applications such as biometric identity authentication, human-computer interaction, and video surveillance. Within the past two decades, numerous face recognition algorithms have been proposed as reviewed in the literature survey. Even though we human beings can detect and identify faces in a cluttered scene with little effort, building an automated system that accomplishes such objective is very challenging. The challenges mainly come from the large variations in the visual stimulus due to illumination conditions, viewing directions, facial expressions, aging, and disguises such as facial hair, glasses, or cosmetics [1]. Face Recognition focuses on recognizing the identity of a person from a database of known individuals. Face Recognition will find countless unobtrusive applications such as airport security and access control, building surveillance and monitoring Human-Computer Intelligent interaction and perceptual interfaces and Smart Environments at home, office and cars [2]. Within the last decade, face recognition (FR) has found a wide range of applications, from identity authentication, access control, and face-based video indexing/browsing, to humancomputer interaction.two issues are central to all these algorithms: 1) feature selection for face representation and 2) classification of a new face image based on the chosen feature representation. This work focuses on the issue of feature selection. Among various solutions to the problem, the most successful are those appearance-based approaches, which generally operate directly on images or appearances of face objects and process the images as two-dimensional (2-D) holistic patterns, to avoid difficulties associated with threedimensional (3-D) modeling, and shape or landmark detection [3]. The initial idea and early work of this research have been published in part as conference papers in [4], [5] and [6]. A recognition process involves a suitable representation, which should make the subsequent processing not only computationally feasible but also robust to certain variations in images. One method of face representation attempts to capture and define the face as a whole and exploit the statistical regularities of pixel intensity variations [7]. The remaining part of this paper is organized as follows. Section II extends to the pattern matching which also introduces and discusses the Chi square test, Entropy and FFNN and SOM in detail. In Section III, extensive experiments on FACE94 and ORL faces are conducted to evaluate the performance of the proposed method on face recognition. Finally, conclusions are drawn in Section IV with some discussions. II. PATTERN MATCHING A. Pattern Recognition Methods During the past 30 years, pattern recognition has had a considerable growth. Applications of pattern recognition now include: character recognition; target detection; medical diagnosis; biomedical signal and image analysis; remote sensing; identification of human faces and of fingerprints; machine part recognition; automatic inspection; and many others. Traditionally, Pattern recognition methods are grouped into two categories: structural methods and feature space methods. Structural methods are useful in situation where the different classes of entity can be distinguished from each other by structural information, e.g. in character recognition different letters of the alphabet are structurally different from each other. The earliest-developed structural methods were the syntactic methods, based on using formal grammars to describe the structure of an entity [8]. The traditional approach to feature-space pattern recognition is the statistical approach, where the boundaries between the regions representing pattern classes in feature space are found by statistical inference based on a design set of sample patterns of known class membership [8]. Feature-space methods are useful in situations where the distinction between different pattern classes is readily expressible in terms of 1 P age

8 International Journal of Computer Science and Application Issue 2010 ISSN Wavelet Tr ansfor m based Feature Extr action for Face Recognition Sangeeta Kakarwal, Ratnadeep Deshmukh Abstract - In this paper, we propose a Wavelet Transform based analysis method for Face Recognition. This algorithm has been used to extract the features of the FERET face database. Results indicate that the proposed methodology is able to achieve excellent performance with only a very small set of features being used, and its error rate is calculated using FAR and FRR. The choice of the Wavelet transform in this setting is motivated by its insensitivity to large variation in light direction, face pose, and facial expression. In the experiments we used Correlation and Threshold values to assure high consistency of the produced classification ou t com es. T h e en cou r a gin g exp er im en t a l r esu lt s demonstrated that the proposed approach by using frontal and side-view images is a feasible and effective solution to recognizing faces, which can lead to a better and practical use of existing forensic databases in computerized human facerecognition applications. KeyWords- AFR (Automatic Face Recognition), FERET, FAR (FalseAcceptance Rate), FRR (False Rejection Rate). I. INTRODUCTION Face recognition from still images and video sequence has been an active research area due to both its scientific challenges and wide range of potential applications such as biometric identity authentication, human-computer interaction, and video surveillance. Within the past two decades, numerous face recognition algorithms have been proposed as reviewed in the literature survey. Even though we human beings can detect and identify faces in a cluttered scene with little effort, building an automated system that accomplishes such objective is very challenging. The challenges mainly come from the large variations in the visual stimulus due to illumination conditions, viewing directions, facial expressions, aging, and disguises such as facial hair, glasses, or cosmetics [1]. Face Recognition focuses on recognizing the identity of a person from a database of known individuals. Face Recognition will find countless unobtrusive applications such as airport security and access control, building surveillance and monitoring Human-Computer Intelligent interaction and perceptual interfaces and Smart Environments at home, office and cars [2]. Within the last decade, face recognition (FR) has found a wide range of applications, from identity authentication, access control, and face-based video indexing/browsing, to human-computer interaction/communication. Two issues are central to all these algorithms: 1) feature selection for face representation and 2) classification of a new face image based on the chosen feature representation. This work focuses on the issue of feature selection. Among various solutions to the problem, the most successful are those appearance-based approaches, which generally operate directly on images or appearances of face objects and process the images as two-dimensional (2-D) holistic patterns, to avoid difficulties associated with threedimensional (3-D) modeling, and shape or landmark detection [3]. The initial idea and early work of this research have been published in part as conference papers in [4], [5]. A recognition process involves two basic computational stages: In a first stage a suitable representation is chosen, which should make the subsequent processing not only computationally feasible but also robust to certain variations in images. One method of face representation attempts to capture and define the face as a whole and exploit the statistical regularities of pixel intensity variations [6]. We have used Wavelet transform to decompose face images and classified it with correlation and different threshold values. The remaining part of this paper is organized as follows. Section II extends to the feature mapping, which also introduces and discusses the Wavelet Transform in detail. In Section III, extensive experiments on FERET databases are conducted to evaluate the performance of the proposed method on face recognition. Finally, conclusions are drawn in Section IV with some discussions. II. PATTERN MATCHING A. Pattern Recognition Methods In communication with the outer world, one of the most important goals for human beings is to recognize objects. For example, from an image, image set, or image sequence of objects, we need to recognize that the objects are oriented toward, where they are located, how they are arranged, what size and shape they have, and what sort of things they are. During the past 30 years, pattern recognition has had a considerable growth. Applications of pattern recognition now include: character recognition; target detection; medical diagnosis; biomedical signal and image analysis; remote sensing; identification of human faces and of fingerprints; machine part recognition; automatic 100

9 REVIEW PAPER International Journal of Recent Trends in Engineering, Vol 2, No. 4, November 2009 Information Theory and Neural Network based Approach for Face Recognition: A Review S.N. Kakarwal 1, Dr. R.R. Deshmukh 2 1 P.E.S. College of Engineering, Department of Computer Science and Engineering, Aurangabad, India s_kakarwal@yahoo.com 2 Dr. Babasaheb Ambedkar Marathwada University, Department of Computer Science and Information Technology, Aurangabad, India ratnadeep_deshmukh@yahoo.co.in Abstract In face recognition, it is important to select the invariant facial features especially faces with various pose and expression changes. This paper presents novel feature extraction techniques such as Entropy and Mutual Information and for classification Feed forward neural network is used, which will be better than traditional methods for accurately recognizing the faces. Index Terms Biometrics, Information Theory, Entropy and mutual information and Feed forward neural network. I. BIOMETRICS Biometric can be defined as technique of studying physical characteristics of a person such as fingerprints, hand geometry, eye structure etc. to establish his or her identity. Biometrics-based personal identification techniques that use physiological or behavioral characteristics are becoming increasingly popular compared to traditional token-based or knowledge based techniques such as identification cards (ID), passwords, etc. One of the main reasons for this popularity is the ability of the biometrics technology to differentiate between an authorized person and an impostor who fraudulently acquires the access privilege of an authorized person [1]. Popular pattern recognition paradigms based on data reduction, such as redundancy reduction and dimensionality reduction, have met with difficulties in solving complex pattern recognition problems, such as the human face recognition problem [2]. A brief description of some commonly used biometrics is given below: A. Face Face recognition is a non-intrusive method, and facial images are probably the most common biometric characteristic used by humans to make personal recognition. The most popular approaches to face recognition are based on either: 1) the location and shape of facial attributes, such as the eyes, eyebrows, nose, lips, and chin and their spatial relationships or 2) the overall (global) analysis of the face image that represents a face as a weighted combination of a number of canonical faces. While the authentication performance of the face recognition systems that are commercially available is reasonable, they impose a number of restrictions on how the facial images are obtained, often requiring a fixed and simple background or special illumination. These systems also have difficulty in matching face images captured 2009 ACADEMY PUBLISHER 176 from two drastically different views and under different illumination conditions (i.e., varying temporal contexts). B. Fingerprint Humans have used fingerprints for personal identification for many decades and the matching (i.e., identification) accuracy using fingerprints has been shown to be very high. A fingerprint is the pattern of ridges and valleys on the surface of a fingertip, the formation of which is determined during the first seven months of fetal development. One problem with the current fingerprint recognition systems is that they require a large amount of computational resources, especially when operating in the identification mode. Finally, fingerprints of a small fraction of the population may be unsuitable for the automatic identification because of genetic factors, aging, environmental, or occupational reasons [3]. C. Retina The retinal vasculature is rich in structure and is supposed to be a characteristic of each individual and each eye. It is claimed to be the most secure biometric since it is not easy to change or replicate the retinal vasculature. The image acquisition requires a person to peep into an eye-piece and focus on a specific spot in the visual field so that a predetermined part of the retinal vasculature could be imaged. The image acquisition involves cooperation of the subject, entails contact with the eyepiece, and requires a conscious effort on the part of the user. All these factors adversely affect the public acceptability of retinal biometric [4]. In the Table I comparison of various Biometric Techniques is given [5]: TABLE I COMPARISON OF VARIOUS BIOMETRIC TECHNIQUES Rank Accuracy Convenience Cost 1 DNA Voice Voice 2 Iris Face Signature 3 Retina Signature Finger 4 Finger Finger Face 5 Face Iris Iris 6 Signature Retina Retina 7 Voice DNA DNA

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